Application developers
Add chat, extraction, recommendations, multimodal generation, automations, and agents to an existing product.
Independent tool overview
Genkit at a glance
Genkit is Google's open-source framework for building, testing, deploying, and observing AI-powered and agentic applications across multiple model providers.
Visit the official Genkit site ↗
- Current name
- Genkit
- Original name
- Firebase Genkit
- Developer
- Google with open-source contributors
- License
- Apache 2.0
- Production-ready SDKs
- JavaScript/TypeScript and Go
- Additional SDKs
- Python beta and Dart preview
- Framework price
- Free
- Deployment
- Firebase, Cloud Run, major clouds, or self-hosted
- Last reviewed
- August 29, 2026
Overview
What Genkit is
Genkit is Google's open-source framework for building full-stack AI features and agents. Originally launched as Firebase Genkit, it now uses the broader Genkit name and genkit.dev home while retaining first-party Firebase and Google Cloud integrations.
The framework gives developers a common programming model for prompts, generation, structured output, streaming, tools, flows, retrieval, and evaluation. Provider plugins cover Google AI and Vertex AI alongside services such as OpenAI, Anthropic, Azure AI Foundry, AWS Bedrock, xAI, DeepSeek, Ollama, and OpenAI-compatible endpoints.
Genkit's local Developer UI is one of its most useful features. Developers can run flows, inspect traces and intermediate steps, test prompts and tools, build evaluation datasets, and compare outputs before deploying an endpoint.
Genkit does not force a Firebase deployment. JavaScript and TypeScript services can run on Cloud Functions for Firebase, Cloud Run, Azure Functions, AWS Lambda, or any Node.js host; Go and Python services can also run on compatible infrastructure. Firebase offers convenient callable functions, authentication context, App Check integration, and production monitoring.
The framework reduces integration work but does not make an AI system safe or reliable by itself. Teams still need to secure every exposed flow, protect provider credentials, validate tool inputs and outputs, constrain permissions, test model behavior, monitor token and infrastructure cost, and review traces for sensitive data.
Use cases
Who Genkit is best for
The strongest fit depends on the job you need the product to complete, not the size of its feature list.
Firebase teams
Expose flows through callable Cloud Functions with familiar Firebase authentication and App Check patterns.
Multi-model applications
Use one generation interface across several providers or swap models as capability, availability, and cost change.
Typed backend workflows
Define schema-validated inputs and outputs around AI features instead of passing unstructured strings through the entire stack.
RAG and tool-use systems
Compose retrievers, embedders, indexes, tools, prompts, and flows for grounded or action-taking features.
Teams improving AI quality
Inspect traces, build datasets, run evaluations, and export production cases for repeatable testing.
Capabilities
Core Genkit features
Unified model API
Calls supported hosted and local models through a consistent generation interface while preserving provider-specific configuration when needed.
Flows
Wraps AI application logic in typed, observable functions that can be tested locally and exposed through deployment adapters.
Structured output
Uses schemas to request and validate machine-readable responses for application logic and user interfaces.
Streaming and multimodal content
Supports incremental responses and model inputs or outputs involving text, images, documents, and other supported media.
Tools and agents
Lets models call developer-defined functions and combines them into multi-step, agentic workflows.
Prompt management
Supports code-defined prompts and Dotprompt templates with input, output, model, and configuration metadata.
RAG abstractions
Provides indexer, embedder, retriever, and document abstractions with plugins for common vector stores and model services.
Developer UI
Runs locally for prompt experiments, flow execution, trace inspection, dataset management, and evaluation review.
Evaluation tooling
Runs manual, heuristic, model-based, and plugin-provided metrics against datasets or production-derived examples.
OpenTelemetry observability
Instruments AI operations with traces, metrics, and logs that can be exported through telemetry plugins.
Flexible deployment
Supports Firebase, Cloud Run, Azure Functions, AWS Lambda, standard Node.js hosts, and language-appropriate self-managed services.
Plugin ecosystem
Extends model providers, retrievers, vector stores, evaluators, deployment adapters, and telemetry exporters through packages.
Process
How the Genkit workflow works
Step 1
Choose a stable SDK
Use JavaScript/TypeScript or Go for production-critical work unless the team accepts the maturity limits of Python beta or Dart preview.
Step 2
Start with one provider
Configure a supported provider and model, store credentials in the deployment platform's secret system, and avoid embedding keys in source code.
Step 3
Define a typed flow
Give the feature a narrow responsibility with validated input, explicit output schema, timeouts, error handling, and predictable failure behavior.
Step 4
Add tools or retrieval deliberately
Grant only the data and actions the feature needs, validate tool arguments, cap retrieved context, and separate read-only from mutating capabilities.
Step 5
Debug locally
Use the CLI and Developer UI to inspect prompts, model responses, tool calls, latency, token use, and intermediate flow steps.
Step 6
Build an evaluation set
Include representative success cases, adversarial input, missing data, injection attempts, tool failures, and outputs that must be rejected.
Step 7
Secure the endpoint
Authenticate callers, verify request integrity where supported, authorize each action, rate-limit abuse, and keep the local Developer UI out of production exposure.
Step 8
Deploy to the right runtime
Choose Firebase, Cloud Run, another cloud, or self-hosting based on language, latency, networking, compliance, scaling, and operational ownership.
Step 9
Monitor production behavior
Track errors, latency, tokens, costs, tool failures, and quality regressions while applying appropriate redaction and trace-retention controls.
Cost
Genkit pricing and free plan
Genkit itself has no license or subscription fee. Real cost comes from model calls, embedding and vector services, hosting, networking, storage, logging, tracing, and any paid third-party plugins or infrastructure.
Genkit framework
Free
The SDKs, CLI, Developer UI, and core framework are open source under Apache 2.0.
- No Genkit license fee
- JavaScript/TypeScript, Go, Python, and Dart SDK tracks
- Open-source repository and plugin interfaces
- Development hardware and engineering time are separate costs
Local models and self-hosting
Infrastructure cost
Run compatible open models through tools such as Ollama and host flows on infrastructure you control.
- No Genkit usage charge
- Compute, accelerator, storage, and operations costs apply
- Model licenses and acceptable-use terms still apply
- The team owns scaling, patching, security, and uptime
Hosted model providers
Provider usage rates
Use Gemini, Claude, OpenAI, Bedrock, Azure, xAI, DeepSeek, or another compatible provider.
- Input, output, caching, media, embedding, and tool prices vary
- Quotas and regional availability depend on the provider
- Model changes can alter both quality and cost
- Use provider budgets and Genkit telemetry to monitor spend
Cloud deployment and monitoring
Usage based
Deploy and observe flows through Firebase, Google Cloud, another cloud, or a managed host.
- Compute, requests, memory, storage, egress, logs, metrics, and traces may be billed
- Firebase Genkit Monitoring requires the Blaze plan
- Google Cloud observability includes free allowances and paid usage
- Total cost depends on traffic and trace volume
Pricing checked . Check current pricing at the source ↗
Assessment
Genkit strengths and limitations
Where it stands out
- Apache-licensed framework can be inspected, extended, and self-hosted
- Unified provider interface reduces model-specific application plumbing
- Typed flows and structured output fit production backend development
- Local Developer UI makes prompts, traces, tools, and intermediate steps easier to debug
- First-party support for tools, RAG, evaluation, and OpenTelemetry covers the AI feature lifecycle
- Firebase and Google Cloud integrations offer a direct path from local flow to hosted endpoint
- Deployment is not limited to Google infrastructure
- Provider and plugin architecture leaves room for hosted, local, and OpenAI-compatible models
What to consider
- Genkit is a developer framework, not a no-code agent builder or hosted end-user product
- Python is still beta and Dart is preview, so their APIs and coverage may change
- Provider abstractions do not eliminate differences in model features, schemas, errors, quotas, or safety behavior
- The framework is free but model, hosting, vector database, logging, and monitoring bills can be substantial
- Developers remain responsible for authentication, authorization, App Check or integrity validation, rate limits, and secret management
- Tool-calling agents can cause real side effects unless permissions and approvals are constrained
- Traces and evaluation datasets may capture prompts, outputs, retrieved context, or other sensitive information
- AI evaluations require representative datasets and human judgment; built-in metrics are not proof that a feature is safe or correct
Compare
Genkit alternatives
The right alternative depends on the specific output, workflow, controls and budget your project requires.
Coding
LangChain
A broader, widely adopted orchestration ecosystem with extensive integrations across Python and JavaScript.
Explore LangChain →Agents
Agent Builder
A more visual, OpenAI-centered option for assembling and versioning agent workflows.
Explore Agent Builder →Coding
Google AI Studio
A faster Gemini-first environment for prompt and model prototyping before building a full application backend.
Explore Google AI Studio →Questions
Genkit FAQs
What is Genkit?
Genkit is Google's open-source framework for building AI-powered and agentic application features with model providers, typed flows, tools, retrieval, evaluation, deployment adapters, and observability.
Is Firebase Genkit still active?
Yes. The project is active and now brands itself primarily as Genkit. Firebase remains an important integration and deployment option, but the framework has a broader genkit.dev home and can run outside Firebase.
Is Genkit free?
The Genkit framework, CLI, and Developer UI are free under Apache 2.0. Model APIs, cloud hosting, databases, storage, monitoring, and other infrastructure are billed separately.
Which languages does Genkit support?
The current project describes JavaScript/TypeScript and Go as production-ready, Python as beta, and Dart as preview. Check the current SDK documentation before committing a production architecture.
Does Genkit work only with Gemini?
No. Official documentation lists plugins for Google AI, Vertex AI, Anthropic, OpenAI, Azure AI Foundry, AWS Bedrock, xAI, DeepSeek, Ollama, and OpenAI-compatible services.
Do I have to deploy Genkit on Firebase?
No. Genkit flows can be deployed through Firebase or Cloud Run, on other major cloud functions, on standard Node.js hosts, or through compatible self-managed services.
What is a Genkit flow?
A flow is a typed, observable function that wraps AI application logic. It can validate inputs and outputs, appear in the Developer UI, expose traces, and be deployed as an API.
Is Genkit production-ready?
The JavaScript/TypeScript and Go SDKs are presented as production-ready, but each application still needs security, evaluation, monitoring, cost controls, error handling, and provider-specific testing before launch.
Bottom line
Our Genkit verdict
Genkit is a strong choice for developers who want a code-first, provider-flexible AI framework with unusually cohesive local debugging, evaluation, deployment, and observability. It is especially attractive to Firebase and Google Cloud teams, but it remains portable. The tradeoff is ownership: the application team must still secure, test, monitor, and pay for every model and service behind the framework.
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